Plant Reliability Assessment Techniques

In Oil & Gas facilities like LNG plants, inspections of aging assets for corrosion damage often require costly production interruptions. Risk-Based Inspection (RBI) changes this. By applying RBI methodology, facilities can optimize and extend inspection intervals—by months or even years—while maintaining (or improving) asset integrity. This is supported by strategic use of non-intrusive inspection techniques between major shutdowns. There are three main types: 1) Qualitative RBI (expert judgement) 2) Quantitative RBI (statistical/probabilistic) 3) Semi-quantitative RBI (hybrid) Standards like API 580, API 581, and DNV-RP-G101 guide credible RBI programs, especially in offshore and industrial environments. These standards help focus inspections on high-risk assets—improving safety and optimizing resources. RBI is now common in oil and gas, petrochemicals, and power generation. The RBI Advantage: Rather than treating all equipment equally, RBI targets resources on assets with the highest probability and consequence of failure. It improves three core areas: 1) Inspection Frequency: Extended intervals based on actual risk, not fixed schedules 2) Inspection Scope: Focused coverage on high-risk components and degradation mechanisms 3) Inspection Techniques: Use of advanced non-intrusive methods like automated Ultrasonics, acoustic emission, and corrosion monitoring tools such as CUI monitoring by CorrosionRADAR Between shutdowns, continuous monitoring provides ongoing asset health insights. This data feeds back into risk models, allowing dynamic updates as equipment conditions evolve. However, one challenge in RBI is risk perception—it varies across engineers and organizations. What’s acceptable at one site may not be at another. RBI programs must be tailored to each organization’s risk tolerance and context. To build an effective RBI program: - Form a multidisciplinary team skilled in both risk assessment and inspection technologies - Use strong data collection to gather historical performance, damage mechanisms, and design data - Commit to continuous improvement: regularly update risk models, use digital tools for real-time monitoring, and integrate feedback from inspectors - Integrate RBI with your maintenance systems to align inspection with actual risk - Promote ongoing training and engagement to build a strong reliability and safety culture *** How is your facility balancing inspection frequency with risk in critical asset monitoring? P.S.: Follow me for more insights on Industry 4.0, Predictive Maintenance, and the future of Corrosion Monitoring.

Predicting failures in complex systems composed of multiple subsystems is a core responsibility for reliability engineers, maintenance planners, and logistics teams. Each subsystem within a product or machine exhibits its own failure probability, typically captured as a reliability curve that quantifies the chance of survival over time. By analyzing these subsystem reliability curves, engineers can anticipate potential points of breakdown, plan for spare parts, and proactively schedule maintenance—helping ensure system uptime and avoiding costly unplanned outages. In practical terms, failure prediction leverages both reliability curves and real-world operational data. For any subsystem, such as SYS1, engineers evaluate the probability of failure at specific points along its operational timeline using the complement of reliability: 1 - Re(t). Aggregating this probability across all deployed units—each with its own service hours—yields a data-driven estimate of how many failures to expect within a fleet. This methodology not only supports logistical preparedness but also provides development teams with a reality check, highlighting discrepancies between predicted and observed field behavior and guiding design refinements for enhanced system reliability.

“Didn’t we fix that pump 6 months ago?” Most plants deal with recurring failures that feel impossible to solve. Sure, we can use Root Cause Analysis to systemically go after these Bad Actors. We can ensure that when something fails, we fix it and improve it so that it won’t fail again. But let’s be a bit more proactive. There is a powerful tool you can use to pre-empt these bad actors. 🟢It's called Failure Modes and Effects Analysis (FMEA). An FMEA is often one of the first steps you would undertake to analyse and improve the reliability of a system or piece of equipment. By using FMEAs on installed equipment that is already operational, we can pre-empt failure. We identify the credible failure modes and determine the best method to address them. During an FMEA, you break the selected equipment down into systems, subsystems, assemblies, and components… and determine how these could fail. You analyse why the failure would happen and what the consequence would be. And the analysis is completed by assigning preventive or corrective actions to improve reliability. An FMEA analysis helps you identify how a piece of equipment might fail. You do this based on experience with similar types of equipment. Or in some cases purely based on sound engineering logic. The main elements of an FMEA are: →The potential failure mode that describes how the item fails to perform as intended; →the cause(s) of the potential failure mode. →The effect of the failure. Either on the system the item is part of or the people using it; Want to know more about FMEA? Want a step-by-step process? Want an editable template? Check out our article, “Why the FMEA is my equipment not reliable?” and download a copy of our FMEA template. Link is in the first comment. #maintenance #reliability #ReliabilityAcademy

When applying methods like HAZOP, FMEA or PHA, the approach consists of decomposing a system into parts, finding possible changes in parameters of processes (e.g., temperature, pressure, flow) or failures of components in installations to anticipate scenarios, calculate their probabilities…and assess levels of risks. Known behaviours (sometimes ‘laws’) of phenomena for instance in thermodynamics or in chemistry are very useful to calculate and anticipate the extent of a pressure or heat increase while knowledge of components’ behaviours and their interactions in equipment allows to imagine possible breakdown, and their implications. Appreciating their likelihood has a subjective side but can also be based on data provided by experience (e.g., frequencies), or calculations. With this type of analysis comes the question of prevention and mitigation measures, and their reasonableness considering what has been analysed. Such techniques were developed from the mid-20th century by engineers precisely for this purpose, in sectors such as aviation, the chemical or nuclear industry, and these principles applied through risk assessment methods to engineered systems have proven their worth. By decomposing the system, by imagining potential breakdowns, by relying on our knowledge of phenomena, by introducing probabilities…such methods provided adequate approaches, and have improved over time. With the ambition of introducing humans (e.g., human reliability assessment - hra) then organisations in risk assessment, several questions were formulated (some time ago now) about the limits of the application of such principles to different kind of phenomena. In this article, I challenged the possibility of borrowing such methodological principles for this purpose. I used the discourse on complexity to make the point, by contrasting technical versus social systems (see visual). https://lnkd.in/egDyf2_B   By creating bridges between natural, technical and social phenomena, system and complexity lenses have made it possible to question methodological principles such as that of risk assessment, also questioning their limits. It triggers many interesting avenues for alternative approaches. Inspiration comes from control theory, system theory, cybernetics, system dynamics, complex adaptive systems, agent-based modeling... These ideas have been variously incorporated in the proposals of system safety engineering, cognitive system engineering (Jens Rasmussen was influential in these traditions, https://lnkd.in/ey_pJwQg, ) but also… in the sociology of safety, and it is this last path that I opened in this article. (note also that the question of the limits of audit was already introduced in this article, https://lnkd.in/ecgxA8tg ).